hrls1 = dsp.RLSFilter(11, 'ForgettingFactor', 0.98);
       hfilt = dsp.FIRFilter('Numerator',fir1(10, .25)); % Unknown System
       x = randn(1000,1);                       % input signal
       d = step(hfilt, x) + 0.01*randn(1000,1); % desired signal
       [y,e] = step(hrls1, x, d);
       w = hrls1.Coefficients;
       figure(1);
       subplot(2,1,1), plot(1:1000, [d,y,e]);
       title('System Identification of an FIR filter');
       legend('Desired', 'Output', 'Error');
       xlabel('time index'); ylabel('signal value');
       subplot(2,1,2); stem([hfilt.Numerator; w].');
       legend('Actual','Estimated'); 
       xlabel('coefficient #'); ylabel('coefficient value'); 
       hrls2 = dsp.RLSFilter('Length', 11, 'Method', 'Householder RLS');
       hfilt2 = dsp.FIRFilter('Numerator',fir1(10, [.5, .75]));
       x = randn(1000,1);                           % Noise
       d = step(hfilt2, x) + sin(0:.05:49.95)';     % Noise + Signal
       [y, err] = step(hrls2, x, d);
       figure(2)
       subplot(2,1,1), plot(d), title('Noise + Signal');
       subplot(2,1,2), plot(err), title('Signal');